Instructions to use kingjones777/Ming-Image-0.1-Design-ROCm-INT8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use kingjones777/Ming-Image-0.1-Design-ROCm-INT8 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("kingjones777/Ming-Image-0.1-Design-ROCm-INT8", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
File size: 1,917 Bytes
18c1466 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 | # coding=utf-8
# Copyright 2024 ANT Group and the HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from transformers import PretrainedConfig
from qwen2_5_vit import Qwen2_5_VLVisionConfig
from configuration_bailing_moe_v2 import BailingMoeV2Config
class BailingMM2Config(PretrainedConfig):
model_type = "bailingmm_moe_v2_lite"
# Declared so transformers' `_attn_implementation` setter recurses into both towers.
# Without it an explicit attn_implementation (e.g. "eager" on ROCm, which has no
# flash-attn) never reaches them, and their "flash_attention_2" defaults raise at
# model construction.
sub_configs = {"vision_config": Qwen2_5_VLVisionConfig, "llm_config": BailingMoeV2Config}
def __init__(
self,
mlp_depth=1,
llm_config: BailingMoeV2Config = None,
vision_config: Qwen2_5_VLVisionConfig = None,
audio_config=None,
**kwargs
):
if audio_config is not None:
raise ValueError("audio_config is not supported by Ming Image inference")
self.audio_config = None
self.vision_config = Qwen2_5_VLVisionConfig(**vision_config) if isinstance(vision_config, dict) else vision_config
self.llm_config = BailingMoeV2Config(**llm_config) if isinstance(llm_config, dict) else llm_config
self.mlp_depth = mlp_depth
super().__init__(**kwargs)
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